Humanistic Organizing: The Transformative Force of Mindful Organizational Communication
Bibliographic record
Abstract
In a workforce characterized by constant disruption across social, economic, and environmental domains, as well as widespread employee suffering in the form of stress, burnout, and disengagement, both scholars and leaders have called for business to undergo transformative change in an effort to bring about collective flourishing. Drawing inspiration from Buddhist philosophy and research on the communicative constitution of organizations, we present a new theory that explains how organizations can foster a positive form of organizing—what we term humanistic organizing—through a tripartite framework of mindful organizational communication. Our theory demonstrates that transforming organizations to become more humanistic is a matter of rethinking an organization’s underlying ethos, grounding that new ethos in humanistic principles, and then: (a) embedding the wisdom of this new ethos in organizational communication, (b) intentionally drawing on this wisdom in everyday talk and text, and (c) ethically enacting this wisdom in the form of ongoing organizing practices, which may include revising the preexisting organizing practices. We illustrate our theory through two case illustrations: one that reflects an organization that has demonstrated humanistic organizing from its inception, and another that reflects an organization that underwent a transformative process to become more humanistic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".